Pajic Receives ACM SIGBED Early Career Researcher Award

Award recognizes outstanding contributions by early career investigators in the area of embedded, real-time and cyber-physical systems

Miroslav Pajic (center) kneels with his research group as they discuss their work on preventing cyber attacks on autonomous vehicles

Miroslav Pajic, the Nortel Networks Assistant Professor of Electrical and Computer Engineering at Duke University, has won this year’s Association for Computing Machinery Special Interest Group on Embedded Systems (ACM SIGBED) Early Career Award.

SIGBED is a focal point within the ACM for all aspects of embedded computing systems, including both software and hardware. Its Early Career Researcher Award recognizes “outstanding contributions by early career investigators in the area of embedded, real-time and cyber-physical systems." Recipients are chosen for their entire body of work, not just a particular project or two, based on their impact in the field.

Pajic’s research focuses on high-assurance system design methodologies for cyber-physical systems (CPS), which feature tight interaction of computing with the physical world. CPS are the next generation of safety-critical embedded control systems, in domains spanning from autonomous vehicles, industrial automation and advanced manufacturing, to medical devices and systems, energy-efficient buildings, and smart cities. Pajic and his students are creating ways to build such systems with strong safety, efficiency and security guarantees.

For example, one of his projects focuses on providing safety guarantees for autonomous vehicles even in the presence of adversarial activities. His approach takes advantage of a car’s interactions with its physical environment to detect attacks and provide resiliency against them. For instance, a digital assailant could compromise the GPS to take an autonomous vehicle off course. But by using previous sensor measurements combined with standard security protocols, Pajic and his team have shown that the car can recognize the false data and act accordingly.